Existing systems for learning-to-rank algorithms are difficult to apply to images. Because text documents contain substantially less data than images and are much more structured (e.g. syntax, semantics, document-level structure) than image data, special care and attention must be taken to ensure that the dimensions of feature vectors used to train a machine-learned model are reasonable. Existing systems cannot simply be converted to apply to image features based on aesthetic quality.
The systems or methods that do exist for assigning an aesthetic score to an image suffer from several drawbacks, including poor performance and being limited to classification rather than ranking. These systems fail to assign an aesthetic score that correlates to intuitive notions of aesthetic quality. The use of such systems to order images by aesthetic quality or to search images by aesthetic quality is inadequate.
With over a trillion photographs being taken every year, many of which are in digital format as images on the Internet, it is a growing problem to be able to manage, sort, search, display, and view such images based on aesthetic quality. Thus there is a need for a system, method, and computer program product that assigns an aesthetic score to an image based on aesthetic quality, and that allows for sets of images to be ordered and searched based on aesthetic quality.
However, aesthetics is subjective. It can vary from person to person and also various firms uses different styles of aesthetics in different use cases. Thus there is need for a system, method, and computer program product that assigns an aesthetic score learned from the previous usage or specified based on examples, that can be adapted for different users, groups of users, demographic groups, segments, usage patterns, and the like.